IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i10p1132-d555897.html
   My bibliography  Save this article

Modeling COVID-19 with Uncertainty in Granada, Spain. Intra-Hospitalary Circuit and Expectations over the Next Months

Author

Listed:
  • José M. Garrido

    (Department of Surgery and Surgical Specialties, University of Granada, 18016 Granada, Spain
    Biosanitary Research Institute of Granada (ibs.GRANADA), 18016 Granada, Spain
    Institute of Biopathology and Regenerative Medicine (IBIMER), University of Granada, 18016 Granada, Spain)

  • David Martínez-Rodríguez

    (Instituto Universitario de Matemática Multidisciplinar, Universitat Politècnica de València, 46022 Valencia, Spain)

  • Fernando Rodríguez-Serrano

    (Biosanitary Research Institute of Granada (ibs.GRANADA), 18016 Granada, Spain
    Institute of Biopathology and Regenerative Medicine (IBIMER), University of Granada, 18016 Granada, Spain)

  • Sorina-M. Sferle

    (Instituto Universitario de Matemática Multidisciplinar, Universitat Politècnica de València, 46022 Valencia, Spain)

  • Rafael-J. Villanueva

    (Instituto Universitario de Matemática Multidisciplinar, Universitat Politècnica de València, 46022 Valencia, Spain)

Abstract

Mathematical models have been remarkable tools for knowing in advance the appropriate time to enforce population restrictions and distribute hospital resources. Here, we present a mathematical Susceptible-Exposed-Infectious-Recovered (SEIR) model to study the transmission dynamics of COVID-19 in Granada, Spain, taking into account the uncertainty of the phenomenon. In the model, the patients moving throughout the hospital’s departments (intra-hospitalary circuit) are considered in order to help to optimize the use of a hospital’s resources in the future. Two main seasons, September–April (autumn-winter) and May–August (summer), where the hospital pressure is significantly different, have been included. The model is calibrated and validated with data obtained from the hospitals in Granada. Possible future scenarios have been simulated. The model is able to capture the history of the pandemic in Granada. It provides predictions about the intra-hospitalary COVID-19 circuit over time and shows that the number of infected is expected to decline continuously from May without an increase next autumn–winter if population measures continue to be satisfied. The model strongly suggests that the number of infected cases will reduce rapidly with aggressive vaccination policies. The proposed study is being used in Granada to design public health policies and perform wise re-distribution of hospital resources in advance.

Suggested Citation

  • José M. Garrido & David Martínez-Rodríguez & Fernando Rodríguez-Serrano & Sorina-M. Sferle & Rafael-J. Villanueva, 2021. "Modeling COVID-19 with Uncertainty in Granada, Spain. Intra-Hospitalary Circuit and Expectations over the Next Months," Mathematics, MDPI, vol. 9(10), pages 1-21, May.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:10:p:1132-:d:555897
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/10/1132/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/9/10/1132/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Avila-Ponce de León, Ugo & Pérez, Ángel G.C. & Avila-Vales, Eric, 2020. "An SEIARD epidemic model for COVID-19 in Mexico: Mathematical analysis and state-level forecast," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    2. Barmparis, G.D. & Tsironis, G.P., 2020. "Estimating the infection horizon of COVID-19 in eight countries with a data-driven approach," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    3. Sarkar, Kankan & Khajanchi, Subhas & Nieto, Juan J., 2020. "Modeling and forecasting the COVID-19 pandemic in India," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    4. Nicole C. J. Brienen & Aura Timen & Jacco Wallinga & Jim E. Van Steenbergen & Peter F. M. Teunis, 2010. "The Effect of Mask Use on the Spread of Influenza During a Pandemic," Risk Analysis, John Wiley & Sons, vol. 30(8), pages 1210-1218, August.
    5. Smriti Mallapaty, 2021. "Are COVID vaccination programmes working? Scientists seek first clues," Nature, Nature, vol. 589(7843), pages 504-505, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mayer Alvo & Jingrui Mu, 2023. "COVID-19 Data Analysis Using Bayesian Models and Nonparametric Geostatistical Models," Mathematics, MDPI, vol. 11(6), pages 1-13, March.
    2. Sharma, Natasha & Verma, Atul Kumar & Gupta, Arvind Kumar, 2021. "Spatial network based model forecasting transmission and control of COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    3. Cooper, Ian & Mondal, Argha & Antonopoulos, Chris G., 2020. "Dynamic tracking with model-based forecasting for the spread of the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    4. Fokas, A.S. & Cuevas-Maraver, J. & Kevrekidis, P.G., 2020. "A quantitative framework for exploring exit strategies from the COVID-19 lockdown," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    5. Samad Noeiaghdam & Sanda Micula & Juan J. Nieto, 2021. "A Novel Technique to Control the Accuracy of a Nonlinear Fractional Order Model of COVID-19: Application of the CESTAC Method and the CADNA Library," Mathematics, MDPI, vol. 9(12), pages 1-26, June.
    6. Taixiang Duan & Hechao Jiang & Xiangshu Deng & Qiongwen Zhang & Fang Wang, 2020. "Government Intervention, Risk Perception, and the Adoption of Protective Action Recommendations: Evidence from the COVID-19 Prevention and Control Experience of China," IJERPH, MDPI, vol. 17(10), pages 1-17, May.
    7. Rabih Ghostine & Mohamad Gharamti & Sally Hassrouny & Ibrahim Hoteit, 2021. "Mathematical Modeling of Immune Responses against SARS-CoV-2 Using an Ensemble Kalman Filter," Mathematics, MDPI, vol. 9(19), pages 1-13, September.
    8. Bandekar, Shraddha Ramdas & Ghosh, Mini, 2022. "A co-infection model on TB - COVID-19 with optimal control and sensitivity analysis," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 200(C), pages 1-31.
    9. de León, Ugo Avila-Ponce & Avila-Vales, Eric & Huang, Kuan-lin, 2022. "Modeling COVID-19 dynamic using a two-strain model with vaccination," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    10. Kafieh, Rahele & Saeedizadeh, Narges & Arian, Roya & Amini, Zahra & Serej, Nasim Dadashi & Vaezi, Atefeh & Javanmard, Shaghayegh Haghjooy, 2020. "Isfahan and Covid-19: Deep spatiotemporal representation," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    11. Rabih Ghostine & Mohamad Gharamti & Sally Hassrouny & Ibrahim Hoteit, 2021. "An Extended SEIR Model with Vaccination for Forecasting the COVID-19 Pandemic in Saudi Arabia Using an Ensemble Kalman Filter," Mathematics, MDPI, vol. 9(6), pages 1-16, March.
    12. Alaeddine Mihoub & Hosni Snoun & Moez Krichen & Montassar Kahia & Riadh Bel Hadj Salah, 2020. "Predicting COVID-19 Spread Level using Socio-Economic Indicators and Machine Learning Techniques," Post-Print hal-03002886, HAL.
    13. da Silva, Ramon Gomes & Ribeiro, Matheus Henrique Dal Molin & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2020. "Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    14. Yeşilkanat, Cafer Mert, 2020. "Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    15. Rubayyi T. Alqahtani & Abdelhamid Ajbar, 2021. "Study of Dynamics of a COVID-19 Model for Saudi Arabia with Vaccination Rate, Saturated Treatment Function and Saturated Incidence Rate," Mathematics, MDPI, vol. 9(23), pages 1-13, December.
    16. Aldila, Dipo, 2020. "Analyzing the impact of the media campaign and rapid testing for COVID-19 as an optimal control problem in East Java, Indonesia," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    17. Matouk, A.E., 2020. "Complex dynamics in susceptible-infected models for COVID-19 with multi-drug resistance," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    18. Zhu, Cheng-Cheng & Zhu, Jiang, 2021. "Dynamic analysis of a delayed COVID-19 epidemic with home quarantine in temporal-spatial heterogeneous via global exponential attractor method," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    19. Rachael M. Jones & Elodie Adida, 2013. "Selecting Nonpharmaceutical Interventions for Influenza," Risk Analysis, John Wiley & Sons, vol. 33(8), pages 1473-1488, August.
    20. Kalantari, Mahdi, 2021. "Forecasting COVID-19 pandemic using optimal singular spectrum analysis," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:10:p:1132-:d:555897. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.